Fast, fully automated MRI-based measurement of the human corpus callosum

Abstract

Event Abstract Back to Event Fast, fully automated MRI-based measurement of the human corpus callosum Timothy J Herron1*, Xiaojian Kang2, And U Turken3 and David L Woods4 1 US Veterans Affairs, Neurology, United States 2 UC Davis, Neurology & Neuroscience, United States 3 US Veterans Affairs Research, United States 4 US Veterans Affairs, United States We present a stand-alone open-source MatLab software package, C8, which measures midsagittal cross-sectional thickness and area of the human corpus callosum from high-resolution T1 in vivo MR images. C8 quickly extracts and measures the callosum using segmented, affine normalized brains as input. Areas are computed for geometrically defined callosal compartments according to several different extant compartment schemes. Thickness is sampled along a median line within the callosum according to three methods of spacing the samples: equal angle, equal-distance, and equal-area. Coregistered accessory input images have values sampled along the median line and near the edges of the callosum. The algorithm’s performance was tested using the high-quality OASIS image database [www.oasis-brains.org] and showed C8 producing consistent, reliable callosal measurement estimates over repeated scans and with values comparable to manually-segmented callosa. We used 1000+ medium quality T1 images from the 1000 Functional Connectomes Project [fcon_1000.projects.nitrc.org] to evaluate between group differences in measured callosum values and regional effects of age and gender on thickness measures. We also sampled normalized T1 values along the callosum to capture regional variation. A second analysis used the OASIS database young normals plus our own high-quality set of T1 images from young normals (~290 subjects total) in order to correlate callosal thickness/area with regional cortical gray matter thickness and area in 34 cortical ROIs defined using FreeSurfer [surfer.nmr.mgh.harvard.edu]. We found that regional callosal area (and callosal thickness to a lesser extent), correlate positively with regional cortical area but not with regional cortical thickness. Figure 1 Keywords: computational neuroscience, Neuroimaging Conference: 4th INCF Congress of Neuroinformatics, Boston, United States, 4 Sep - 6 Sep, 2011. Presentation Type: Poster Presentation Topic: Computational neuroscience Citation: Herron T, Kang X, Turken A and Woods D (2011). Fast, fully automated MRI-based measurement of the human corpus callosum. Front. Neuroinform. Conference Abstract: 4th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2011.08.00108 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 17 Oct 2011; Published Online: 19 Oct 2011. * Correspondence: Dr. Timothy J Herron, US Veterans Affairs, Neurology, Martinez, United States, tjherron@ebire.org Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Timothy J Herron Xiaojian Kang And U Turken David L Woods Google Timothy J Herron Xiaojian Kang And U Turken David L Woods Google Scholar Timothy J Herron Xiaojian Kang And U Turken David L Woods PubMed Timothy J Herron Xiaojian Kang And U Turken David L Woods Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.